Introduction

The objective of this project was to wrangle WeRateDogs Twitter data to create interesting and reliable analyses and visualization. WeRatesDogs downloaded their Twitter data and provided it to Udacity for us. Beside gathering, assessing, and cleaning, it was required to come up with this analysis and visualization report.

Insights about Data (Analysis and Visualizations)

Source Distribution

While wrangling the datasets, I wondered about the main source of the tweets. I wanted to know about the tweets source distribution. It is noticible that main source of the tweets come from smart devices in this case iPhones. Twitter Mobile App is desire user application and Tweet Web Client is not.

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Correlation

After visually inspecting our new dataset twitter_archive_master, I though that there was a positive correlation between favorites and retweet. the picture below show us that there is a positive correlation between favorites and retweets. We can confirm that the favotires tweet usually gets the most retweets.

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dog_type Distribution

I needed to do some research online to actually understand the meaning of doggo, pupper, puppo, floofer. These words are an Internet Language Built Around Love For The Puppers, also known as DoggoLingo. DoggoLingo, a language trend that's been gaining steam on the Internet in the past few years. The language most often accompanies a picture or a video of a dog and has spread to all major forms of social media. It might even change the way we talk out loud to our beloved canines.
Doggo: Catchall term for members of the canine species.

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Pupper: Another term for members of the canine species, often used to specifically identify puppies or smaller dogs (sometimes with the adjective “smol”).

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Let's take a look at the distribution of dog_type

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Conclusion

In [ ]: